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# Copyright (c) 2024 NVIDIA CORPORATION.
# Licensed under the MIT license.
import torch
import torch.nn as nn
from alias_free_activation.torch.resample import UpSample1d, DownSample1d
# load fused CUDA kernel: this enables importing anti_alias_activation_cuda
from alias_free_activation.cuda import load
anti_alias_activation_cuda = load.load()
class FusedAntiAliasActivation(torch.autograd.Function):
"""
Assumes filter size 12, replication padding on upsampling/downsampling, and logscale alpha/beta parameters as inputs.
The hyperparameters are hard-coded in the kernel to maximize speed.
NOTE: The fused kenrel is incorrect for Activation1d with different hyperparameters.
"""
@staticmethod
def forward(ctx, inputs, up_ftr, down_ftr, alpha, beta):
activation_results = anti_alias_activation_cuda.forward(
inputs, up_ftr, down_ftr, alpha, beta
)
return activation_results
@staticmethod
def backward(ctx, output_grads):
raise NotImplementedError
return output_grads, None, None
class Activation1d(nn.Module):
def __init__(
self,
activation,
up_ratio: int = 2,
down_ratio: int = 2,
up_kernel_size: int = 12,
down_kernel_size: int = 12,
fused: bool = True,
):
super().__init__()
self.up_ratio = up_ratio
self.down_ratio = down_ratio
self.act = activation
self.upsample = UpSample1d(up_ratio, up_kernel_size)
self.downsample = DownSample1d(down_ratio, down_kernel_size)
self.fused = fused # Whether to use fused CUDA kernel or not
def forward(self, x):
if not self.fused:
x = self.upsample(x)
x = self.act(x)
x = self.downsample(x)
return x
else:
if self.act.__class__.__name__ == "Snake":
beta = self.act.alpha.data # Snake uses same params for alpha and beta
else:
beta = (
self.act.beta.data
) # Snakebeta uses different params for alpha and beta
alpha = self.act.alpha.data
if (
not self.act.alpha_logscale
): # Exp baked into cuda kernel, cancel it out with a log
alpha = torch.log(alpha)
beta = torch.log(beta)
x = FusedAntiAliasActivation.apply(
x, self.upsample.filter, self.downsample.lowpass.filter, alpha, beta
)
return x
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